add qwen3

vllm-v0.6.2/vllm/scripts.py

@@ -0,0 +1,201 @@
+# The CLI entrypoint to vLLM.
+import argparse
+import os
+import signal
+import sys
+from typing import List, Optional
+
+import uvloop
+from openai import OpenAI
+from openai.types.chat import ChatCompletionMessageParam
+
+from vllm.engine.arg_utils import EngineArgs
+from vllm.entrypoints.openai.api_server import run_server
+from vllm.entrypoints.openai.cli_args import (make_arg_parser,
+                                              validate_parsed_serve_args)
+from vllm.logger import init_logger
+from vllm.utils import FlexibleArgumentParser
+
+logger = init_logger(__name__)
+
+
+def register_signal_handlers():
+
+    def signal_handler(sig, frame):
+        sys.exit(0)
+
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTSTP, signal_handler)
+
+
+def serve(args: argparse.Namespace) -> None:
+    # The default value of `--model`
+    if args.model != EngineArgs.model:
+        raise ValueError(
+            "With `vllm serve`, you should provide the model as a "
+            "positional argument instead of via the `--model` option.")
+
+    # EngineArgs expects the model name to be passed as --model.
+    args.model = args.model_tag
+
+    uvloop.run(run_server(args))
+
+
+def interactive_cli(args: argparse.Namespace) -> None:
+    register_signal_handlers()
+
+    base_url = args.url
+    api_key = args.api_key or os.environ.get("OPENAI_API_KEY", "EMPTY")
+    openai_client = OpenAI(api_key=api_key, base_url=base_url)
+
+    if args.model_name:
+        model_name = args.model_name
+    else:
+        available_models = openai_client.models.list()
+        model_name = available_models.data[0].id
+
+    print(f"Using model: {model_name}")
+
+    if args.command == "complete":
+        complete(model_name, openai_client)
+    elif args.command == "chat":
+        chat(args.system_prompt, model_name, openai_client)
+
+
+def complete(model_name: str, client: OpenAI) -> None:
+    print("Please enter prompt to complete:")
+    while True:
+        input_prompt = input("> ")
+
+        completion = client.completions.create(model=model_name,
+                                               prompt=input_prompt)
+        output = completion.choices[0].text
+        print(output)
+
+
+def chat(system_prompt: Optional[str], model_name: str,
+         client: OpenAI) -> None:
+    conversation: List[ChatCompletionMessageParam] = []
+    if system_prompt is not None:
+        conversation.append({"role": "system", "content": system_prompt})
+
+    print("Please enter a message for the chat model:")
+    while True:
+        input_message = input("> ")
+        conversation.append({"role": "user", "content": input_message})
+
+        chat_completion = client.chat.completions.create(model=model_name,
+                                                         messages=conversation)
+
+        response_message = chat_completion.choices[0].message
+        output = response_message.content
+
+        conversation.append(response_message)  # type: ignore
+        print(output)
+
+
+def _add_query_options(
+        parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
+    parser.add_argument(
+        "--url",
+        type=str,
+        default="http://localhost:8000/v1",
+        help="url of the running OpenAI-Compatible RESTful API server")
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default=None,
+        help=("The model name used in prompt completion, default to "
+              "the first model in list models API call."))
+    parser.add_argument(
+        "--api-key",
+        type=str,
+        default=None,
+        help=(
+            "API key for OpenAI services. If provided, this api key "
+            "will overwrite the api key obtained through environment variables."
+        ))
+    return parser
+
+
+def env_setup():
+    # The safest multiprocessing method is `spawn`, as the default `fork` method
+    # is not compatible with some accelerators. The default method will be
+    # changing in future versions of Python, so we should use it explicitly when
+    # possible.
+    #
+    # We only set it here in the CLI entrypoint, because changing to `spawn`
+    # could break some existing code using vLLM as a library. `spawn` will cause
+    # unexpected behavior if the code is not protected by
+    # `if __name__ == "__main__":`.
+    #
+    # References:
+    # - https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
+    # - https://pytorch.org/docs/stable/notes/multiprocessing.html#cuda-in-multiprocessing
+    # - https://pytorch.org/docs/stable/multiprocessing.html#sharing-cuda-tensors
+    # - https://docs.habana.ai/en/latest/PyTorch/Getting_Started_with_PyTorch_and_Gaudi/Getting_Started_with_PyTorch.html?highlight=multiprocessing#torch-multiprocessing-for-dataloaders
+    if "VLLM_WORKER_MULTIPROC_METHOD" not in os.environ:
+        logger.debug("Setting VLLM_WORKER_MULTIPROC_METHOD to 'spawn'")
+        os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+
+def main():
+    env_setup()
+
+    parser = FlexibleArgumentParser(description="vLLM CLI")
+    subparsers = parser.add_subparsers(required=True, dest="subparser")
+
+    serve_parser = subparsers.add_parser(
+        "serve",
+        help="Start the vLLM OpenAI Compatible API server",
+        usage="vllm serve <model_tag> [options]")
+    serve_parser.add_argument("model_tag",
+                              type=str,
+                              help="The model tag to serve")
+    serve_parser.add_argument(
+        "--config",
+        type=str,
+        default='',
+        required=False,
+        help="Read CLI options from a config file."
+        "Must be a YAML with the following options:"
+        "https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#command-line-arguments-for-the-server"
+    )
+    serve_parser = make_arg_parser(serve_parser)
+    serve_parser.set_defaults(dispatch_function=serve)
+
+    complete_parser = subparsers.add_parser(
+        "complete",
+        help=("Generate text completions based on the given prompt "
+              "via the running API server"),
+        usage="vllm complete [options]")
+    _add_query_options(complete_parser)
+    complete_parser.set_defaults(dispatch_function=interactive_cli,
+                                 command="complete")
+
+    chat_parser = subparsers.add_parser(
+        "chat",
+        help="Generate chat completions via the running API server",
+        usage="vllm chat [options]")
+    _add_query_options(chat_parser)
+    chat_parser.add_argument(
+        "--system-prompt",
+        type=str,
+        default=None,
+        help=("The system prompt to be added to the chat template, "
+              "used for models that support system prompts."))
+    chat_parser.set_defaults(dispatch_function=interactive_cli, command="chat")
+
+    args = parser.parse_args()
+    if args.subparser == "serve":
+        validate_parsed_serve_args(args)
+
+    # One of the sub commands should be executed.
+    if hasattr(args, "dispatch_function"):
+        args.dispatch_function(args)
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()